{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-artelab--mbart-summarization-fanpage","slug":"artelab--mbart-summarization-fanpage","name":"mbart-summarization-fanpage","type":"model","url":"https://huggingface.co/ARTeLab/mbart-summarization-fanpage","page_url":"https://unfragile.ai/artelab--mbart-summarization-fanpage","categories":["model-training"],"tags":["transformers","pytorch","safetensors","mbart","text2text-generation","summarization","it","dataset:ARTeLab/fanpage","base_model:facebook/mbart-large-cc25","base_model:finetune:facebook/mbart-large-cc25","endpoints_compatible","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-artelab--mbart-summarization-fanpage__cap_0","uri":"capability://text.generation.language.multilingual.abstractive.summarization.with.language.preservation","name":"multilingual-abstractive-summarization-with-language-preservation","description":"Performs abstractive summarization across 25 languages using mBART's encoder-decoder transformer architecture, which encodes source text in any of 25 supported languages and decodes abstractive summaries while preserving the source language. The model was fine-tuned on the ARTeLab/fanpage dataset (Italian fan community discussions) using sequence-to-sequence loss, enabling it to generate coherent summaries that capture semantic meaning rather than extracting sentences. Language detection and routing are implicit in the mBART tokenizer, which uses language-specific tokens to signal the target language during decoding.","intents":["I need to automatically summarize user-generated content from multilingual social media or fan communities while maintaining the original language","I want to reduce long discussion threads or posts into concise summaries for content moderation or analytics workflows","I need to batch-process Italian-language text summaries at scale without calling external APIs"],"best_for":["teams building content moderation systems for multilingual platforms","developers creating summarization pipelines for fan communities or social media aggregation","researchers fine-tuning mBART for domain-specific summarization tasks"],"limitations":["Fine-tuned specifically on Italian fanpage data — performance on other languages degrades compared to base mBART, especially for non-European languages","Abstractive summaries may hallucinate facts not present in source text due to transformer attention patterns — requires human review for high-stakes applications","Input length limited to ~1024 tokens (roughly 4000 characters) due to mBART's positional embeddings; longer documents require chunking strategies","No built-in confidence scores or uncertainty quantification — cannot distinguish high-confidence from low-confidence summaries","Inference latency ~2-5 seconds per document on CPU; GPU acceleration required for production throughput"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+","Hugging Face transformers library 4.0+","4GB+ RAM for model loading (model size ~610MB in fp32, ~305MB in fp16)","Python 3.7+"],"input_types":["raw text (UTF-8 encoded, any of 25 mBART languages)","pre-tokenized text (if using custom tokenization)","structured text with metadata (metadata ignored by model)"],"output_types":["text (abstractive summary in same language as input)","token IDs (if using model.generate() with return_tensors='pt')","attention weights (if using output_attentions=True for interpretability)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-artelab--mbart-summarization-fanpage__cap_1","uri":"capability://tool.use.integration.batch.inference.with.huggingface.inference.api","name":"batch-inference-with-huggingface-inference-api","description":"Integrates with Hugging Face Inference API endpoints (marked as 'endpoints_compatible' in model card) to enable serverless batch summarization without managing GPU infrastructure. Requests are routed to Hugging Face's managed inference servers, which handle model loading, batching, and auto-scaling. The API accepts HTTP POST requests with JSON payloads containing input text and optional generation parameters (max_length, num_beams, temperature), returning JSON responses with generated summaries and optional metadata.","intents":["I want to summarize documents without provisioning or managing GPU servers","I need to integrate summarization into a web application with minimal backend infrastructure","I want to scale summarization from 10 to 10,000 requests per day without code changes"],"best_for":["startups and small teams without ML infrastructure expertise","web applications requiring on-demand summarization without batch processing","prototyping and MVP development where infrastructure cost matters"],"limitations":["API latency ~1-3 seconds per request plus network round-trip time — unsuitable for real-time applications requiring <500ms response times","Pricing scales with request volume — batch processing 1M documents monthly becomes expensive vs self-hosted GPU","Rate limiting and quota enforcement — free tier limited to ~30 requests/minute","Cold start latency when model is unloaded from inference server (~5-10 seconds on first request after idle period)","No local model caching — every request incurs network overhead"],"requires":["Hugging Face API token (free or paid account)","HTTP client library (requests in Python, fetch in JavaScript, etc.)","Network connectivity to api-inference.huggingface.co"],"input_types":["JSON payload with 'inputs' field containing text string","optional 'parameters' object with generation hyperparameters"],"output_types":["JSON response with 'summary_text' field","optional token usage metadata if requested"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-artelab--mbart-summarization-fanpage__cap_2","uri":"capability://code.generation.editing.local.cpu.inference.with.transformers.pipeline","name":"local-cpu-inference-with-transformers-pipeline","description":"Supports direct inference via Hugging Face transformers library's high-level pipeline API, which abstracts tokenization, model loading, and decoding into a single function call. The pipeline automatically downloads the model from Hugging Face Hub, caches it locally, and handles device placement (CPU or GPU). For summarization, the pipeline wraps the mBART model with a SummarizationPipeline class that manages input preprocessing (truncation to max_length), generation (beam search decoding), and output formatting.","intents":["I want to add summarization to a Python script with minimal boilerplate code","I need to run summarization locally on a laptop or edge device without cloud dependencies","I want to experiment with different generation parameters (beam size, length penalty) without managing low-level tensor operations"],"best_for":["Python developers building NLP applications with minimal ML infrastructure","researchers prototyping summarization pipelines before production deployment","offline applications requiring local model inference without internet connectivity"],"limitations":["CPU inference is slow — ~5-15 seconds per document on modern CPUs vs <1 second on GPU, making real-time applications impractical","Memory footprint ~1.2GB for model + tokenizer + Python runtime — requires 4GB+ RAM systems","No built-in batching optimization in pipeline API — processing 100 documents sequentially is slower than batching them together","Pipeline API abstracts away low-level control — difficult to customize tokenization, attention masking, or generation strategies","First inference call incurs ~2-3 second model loading overhead (subsequent calls reuse cached model)"],"requires":["Python 3.7+","transformers library 4.0+","torch 1.9+ (CPU or GPU)","4GB+ RAM","Internet connectivity for first-time model download (~650MB)"],"input_types":["Python string","list of strings (for batch processing)","file path (if wrapped in custom code)"],"output_types":["list of dictionaries with 'summary_text' key","raw tensor outputs (if using model.generate() directly)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-artelab--mbart-summarization-fanpage__cap_3","uri":"capability://code.generation.editing.fine.tuning.on.custom.summarization.datasets","name":"fine-tuning-on-custom-summarization-datasets","description":"Model weights are available in safetensors format (safer than pickle, supports memory-mapping) and can be loaded as a starting point for fine-tuning on custom datasets. The fine-tuning process uses the Hugging Face Trainer API, which implements distributed training, gradient accumulation, mixed-precision training (fp16), and automatic learning rate scheduling. Fine-tuning leverages the model's pre-trained mBART weights (trained on 25 languages) as initialization, requiring only 10-20% of the data needed to train from scratch.","intents":["I want to adapt this model to summarize domain-specific text (e.g., medical records, legal documents, technical specifications) with better quality than the generic model","I need to fine-tune on my proprietary dataset without sharing data with external APIs","I want to create a specialized summarization model for a specific language or dialect not well-covered by the base model"],"best_for":["teams with domain-specific summarization needs and labeled training data (500+ examples)","organizations with proprietary data that cannot be sent to cloud APIs","researchers extending mBART for new languages or specialized domains"],"limitations":["Requires labeled training data (source text + reference summaries) — annotation cost is significant for large datasets","Fine-tuning requires GPU access — training on 10K examples takes ~2-4 hours on A100 GPU, making experimentation expensive","Hyperparameter tuning is non-trivial — learning rate, batch size, and warmup steps significantly impact final model quality, requiring validation set evaluation","Catastrophic forgetting risk — aggressive fine-tuning on small datasets can degrade multilingual capability of base model","No built-in evaluation metrics — requires manual ROUGE/BLEU score computation or external evaluation framework"],"requires":["Python 3.7+","transformers library 4.0+","datasets library for data loading","torch 1.9+ with CUDA support (GPU strongly recommended)","8GB+ VRAM for batch_size=8; 16GB+ for batch_size=16","labeled dataset in CSV or JSON format with 'text' and 'summary' fields"],"input_types":["CSV file with 'text' and 'summary' columns","JSON Lines format with 'text' and 'summary' keys","Hugging Face Dataset object"],"output_types":["fine-tuned model weights (PyTorch format or safetensors)","training logs with loss curves and validation metrics","model card with hyperparameters and performance metrics"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-artelab--mbart-summarization-fanpage__cap_4","uri":"capability://text.generation.language.multilingual.language.routing.via.mbart.tokenizer","name":"multilingual-language-routing-via-mbart-tokenizer","description":"The mBART tokenizer includes language-specific tokens (e.g., 'it_IT' for Italian, 'en_XX' for English) that signal the target language during decoding. When generating summaries, the model uses these tokens to route attention and vocabulary selection appropriately. The tokenizer automatically detects input language from the source text (via language detection heuristics or explicit language specification) and prepends the corresponding language token to the decoder input, enabling the same model to generate summaries in any of 25 supported languages without separate language-specific models.","intents":["I want to summarize content in multiple languages using a single model without maintaining separate language-specific models","I need to preserve the source language in summaries (e.g., summarize Italian text in Italian, not English)","I want to cross-lingual summarization where input and output languages differ (e.g., summarize English text in Italian)"],"best_for":["multilingual platforms (e.g., international social media, global news aggregators) requiring single-model deployment","teams without resources to maintain separate models per language","applications requiring language-preserving summarization across diverse user bases"],"limitations":["Language detection is implicit and imperfect — code-mixed text or very short inputs may be misclassified, leading to incorrect language routing","Performance varies significantly across languages — model performs best on high-resource languages (English, Spanish, French) and degrades on low-resource languages (e.g., Urdu, Vietnamese)","No explicit language specification in pipeline API — requires custom code to override language detection if needed","Cross-lingual summarization (input language ≠ output language) is not explicitly trained and may produce lower-quality summaries","Language token overhead adds ~2-3 tokens to every input, slightly reducing effective context length"],"requires":["transformers library 4.0+ with mBART tokenizer support","input text in one of 25 supported mBART languages (ar_AR, cs_CZ, de_DE, en_XX, es_XX, et_EE, fi_FI, fr_XX, gu_IN, hi_IN, it_IT, ja_XX, kk_KZ, ko_KR, lt_LT, lv_LV, my_MM, ne_NP, nl_XX, pt_XX, ro_RO, ru_RU, si_LK, tr_TR, zh_CN)"],"input_types":["text in any of 25 supported languages","language code (optional, for explicit language specification)"],"output_types":["text in the same language as input (or specified target language)","language token metadata (if using low-level API)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-artelab--mbart-summarization-fanpage__cap_5","uri":"capability://text.generation.language.sequence.to.sequence.generation.with.beam.search.decoding","name":"sequence-to-sequence-generation-with-beam-search-decoding","description":"Generates summaries using beam search decoding (not greedy decoding), which explores multiple hypothesis sequences in parallel and selects the highest-probability sequence. The model's generate() method supports configurable beam width (num_beams parameter, typically 4-8), length penalty (to balance summary length), and early stopping. Beam search trades inference latency (~2-5x slower than greedy) for summary quality, as it considers multiple decoding paths rather than committing to the highest-probability token at each step.","intents":["I want to generate higher-quality summaries than greedy decoding produces, even if it takes longer","I need to control summary length and prevent overly short or long summaries via length penalties","I want to generate multiple candidate summaries (num_return_sequences) for ranking or ensemble methods"],"best_for":["applications where summary quality is critical (e.g., legal document summarization, medical record summarization)","batch processing workflows where latency is less critical than quality","research and evaluation pipelines comparing different decoding strategies"],"limitations":["Beam search is 2-5x slower than greedy decoding — inference time increases from ~1-2 seconds to ~5-10 seconds per document","Beam width is a hyperparameter requiring tuning — larger beams (num_beams=8) produce better summaries but slower inference","Length penalty tuning is non-intuitive — requires experimentation to find values that produce desired summary lengths","Beam search does not guarantee optimal solutions — only approximates the highest-probability sequence due to finite beam width","Memory usage scales with beam width — num_beams=8 uses ~8x more GPU memory than num_beams=1"],"requires":["transformers library 4.0+","PyTorch or TensorFlow backend","GPU recommended for reasonable latency (CPU beam search is very slow)"],"input_types":["tokenized input IDs (tensor)","attention mask (tensor, optional)"],"output_types":["generated token IDs (tensor)","sequence scores (if return_dict_in_generate=True)","decoded text summaries (if using tokenizer.decode())"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"high","permissions":["PyTorch 1.9+ or TensorFlow 2.4+","Hugging Face transformers library 4.0+","4GB+ RAM for model loading (model size ~610MB in fp32, ~305MB in fp16)","Python 3.7+","Hugging Face API token (free or paid account)","HTTP client library (requests in Python, fetch in JavaScript, etc.)","Network connectivity to api-inference.huggingface.co","transformers library 4.0+","torch 1.9+ (CPU or GPU)","4GB+ RAM"],"failure_modes":["Fine-tuned specifically on Italian fanpage data — performance on other languages degrades compared to base mBART, especially for non-European languages","Abstractive summaries may hallucinate facts not present in source text due to transformer attention patterns — requires human review for high-stakes applications","Input length limited to ~1024 tokens (roughly 4000 characters) due to mBART's positional embeddings; longer documents require chunking strategies","No built-in confidence scores or uncertainty quantification — cannot distinguish high-confidence from low-confidence summaries","Inference latency ~2-5 seconds per document on CPU; GPU acceleration required for production throughput","API latency ~1-3 seconds per request plus network round-trip time — unsuitable for real-time applications requiring <500ms response times","Pricing scales with request volume — batch processing 1M documents monthly becomes expensive vs self-hosted GPU","Rate limiting and quota enforcement — free tier limited to ~30 requests/minute","Cold start latency when model is unloaded from inference server (~5-10 seconds on first request after idle period)","No local model caching — every request incurs network overhead","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.41782814234787313,"quality":0.22,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.764Z","last_scraped_at":"2026-05-03T14:22:54.515Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":40872,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=artelab--mbart-summarization-fanpage","compare_url":"https://unfragile.ai/compare?artifact=artelab--mbart-summarization-fanpage"}},"signature":"XxeE80cPt7GB2GwavsxCTeICK5ApqOsKQ7S3taxvzBoAy0hPrCU+GsvlEBv+PRYy6rCWanSU4x90uFVWE5YZBQ==","signedAt":"2026-06-21T05:55:06.719Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/artelab--mbart-summarization-fanpage","artifact":"https://unfragile.ai/artelab--mbart-summarization-fanpage","verify":"https://unfragile.ai/api/v1/verify?slug=artelab--mbart-summarization-fanpage","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}